In this last decade, air transport has become one of the most popular means of transport
among the population, due to the reduction in the price and the improvement of airport
infrastructures. As a consecuence, the number of daily flights grows in an incessant way.
Currently, there are more than 100,000 flights every day that need to be controlled and
managed by air traffic management systems. This work examines how Big Data technologies
can help in the development of new and more efficient air traffic control systems.
The design of a Big Data architecture is proposed following a Data Lake design. This
architecture is implemented using the Hadoop distributed processing framework, that will
allow us to transform large volumes of data into useful information for air traffic management.
We use air navigation surveillance data from multiple providers to obtain a wide
coverage that allows us to know the trajectory that aircraft have made since takeoff to landing.
These surveillance data are enriched with air control data to generate information of
greater value. An evaluation of the generated data is made and a comparison that allows us
to affirm that, the use of multiple surveillance information providers allows reconstructing
more complete flown trajectories. Therefore, the trajectories obtained through the use of
multiple providers will provide more information to the air control systems.
